The glycolytic enzymes activity in the midgut of diabrotica virgifera virgifera (Coleoptera: Chrysomelidae) adult and their seasonal changes

The western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae) is an important pest of maize. The diet of the D. virgifera imago is rich in starch and other polysaccharides present in cereals such as maize. Therefore, knowledge about enzymes involved in digestion of such specific food of this pest seems to be important. The paper shows, for the first time, the activities of main glycolytic enzymes in the midgut of D. virgifera imago: endoglycosidases (a-amylase, cellulase, chitinase, licheninase, laminarinase); exoglycosidases (a- and b-glucosidases, a- and b-galactosidases) and disaccharidases (maltase, isomaltase, sucrase, trehalase, lactase, and cellobiase). Activities of a-amylase, a-glucosidase, and maltase were the highest among assayed endoglycosidases, exoglycosidases, and disaccharidases, respectively. This indicates that in the midgut of D. virgifera imago a-amylase, a-glucosidase and maltase are important enzymes in starch hydrolysis and products of its digestion. These results lead to conclusion that inhibition of most active glycolytic enzymes of D. virgifera imago may be another promising method for chemical control of this pest of maize

Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.Peer Reviewe

Numerical modelling of Tb3+ doped selenide-chalcogenide multimode fibre based spontaneous emission sources

A model is developed of a terbium (III) ion doped selenide chalcogenide glass fibre source that provides spontaneous emission within the mid-infrared (MIR) wavelength range. Three numerical algorithms are used to calculate the solution and compare their properties

Beyond the low frequency fluctuations : morning and evening differences in human brain

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-h-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-h clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. Twenty-nine extreme morning- and 34 evening-type participants underwent two fMRI sessions: about 1 h after wake-up time (morning) and about 10 h after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning (MS) and evening (ES) sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology

Modelling of multimode selenide-chalcogenide glass fibre based MIR spontaneous emission sources

Chalcogenide glass fibres have been demonstrated as a suitable medium for the realisation of spontaneous emission sources for mid-infrared photonics applications with a particular emphasis on sensor technology. Such sources give a viable alternative to other solutions due to their potentially low cost, high reliability and robustness when pumped using commercially available semiconductor lasers. We present a comprehensive analysis of the properties of selenide-chalcogenide glass fibres applied as spontaneous emission sources. We extract the modelling parameters from measurements using in house fabricated bulk glass and fibre samples. We apply the well-established rate equations approach to determine the level populations, the distribution of the photon intensity within the fibre and the output power levels. We compare the modelling results with experiment

Identifying diurnal variability of brain connectivity patterns using graph theory

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks